---
title: "NvidiaGenerator"
id: nvidiagenerator
slug: "/nvidiagenerator"
description: "This Generator enables text generation using Nvidia-hosted models."
---

# NvidiaGenerator

This Generator enables text generation using Nvidia-hosted models.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [`PromptBuilder`](../builders/promptbuilder.mdx) |
| **Mandatory init variables** | `api_key`: API key for the NVIDIA NIM. Can be set with `NVIDIA_API_KEY` env var. |
| **Mandatory run variables** | `prompt`: A string containing the prompt for the LLM |
| **Output variables** | `replies`: A list of strings with all the replies generated by the LLM  <br /> <br />`meta`: A list of dictionaries with the metadata associated with each reply, such as token count and others |
| **API reference** | [Nvidia](/reference/integrations-nvidia) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/nvidia |

</div>

## Overview

The `NvidiaGenerator` provides an interface for generating text using LLMs self-hosted with NVIDIA NIM or models hosted on the [NVIDIA API catalog](https://build.nvidia.com/explore/discover).

## Usage

To start using `NvidiaGenerator`, first, install the `nvidia-haystack` package:

```shell
pip install nvidia-haystack
```

You can use the `NvidiaGenerator` with all the LLMs available in the [NVIDIA API catalog](https://docs.api.nvidia.com/nim/reference) or a model deployed with NVIDIA NIM. Follow the [NVIDIA NIM for LLMs Playbook](https://developer.nvidia.com/docs/nemo-microservices/inference/playbooks/nmi_playbook.html) to learn how to deploy your desired model on your infrastructure.

### On its own

To use LLMs from the NVIDIA API catalog, you need to specify the correct `api_url` and your API key. You can get your API key directly from the [catalog website](https://build.nvidia.com/explore/discover).

The `NvidiaGenerator` needs an Nvidia API key to work. It uses the `NVIDIA_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`, as in the following example.

```python
from haystack.utils.auth import Secret
from haystack_integrations.components.generators.nvidia import NvidiaGenerator

generator = NvidiaGenerator(
    model="meta/llama3-70b-instruct",
    api_url="https://integrate.api.nvidia.com/v1",
    api_key=Secret.from_token("<your-api-key>"),
    model_arguments={
        "temperature": 0.2,
        "top_p": 0.7,
        "max_tokens": 1024,
    },
)
generator.warm_up()

result = generator.run(prompt="What is the answer?")
print(result["replies"])
print(result["meta"])
```

To use a locally deployed model, you need to set the `api_url` to your localhost and unset your `api_key`.

```python
from haystack_integrations.components.generators.nvidia import NvidiaGenerator

generator = NvidiaGenerator(
    model="llama-2-7b",
    api_url="http://0.0.0.0:9999/v1",
    api_key=None,
    model_arguments={
        "temperature": 0.2,
    },
)
generator.warm_up()

result = generator.run(prompt="What is the answer?")
print(result["replies"])
print(result["meta"])
```

### In a Pipeline

Here's an example of a RAG pipeline:

```python
from haystack import Pipeline, Document
from haystack.utils.auth import Secret
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.generators.nvidia import NvidiaGenerator

docstore = InMemoryDocumentStore()
docstore.write_documents([Document(content="Rome is the capital of Italy"), Document(content="Paris is the capital of France")])

query = "What is the capital of France?"

template = """
Given the following information, answer the question.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: {{ query }}?
"""
pipe = Pipeline()

pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
pipe.add_component("prompt_builder", PromptBuilder(template=template))
pipe.add_component("llm", NvidiaGenerator(
    model="meta/llama3-70b-instruct",
    api_url="https://integrate.api.nvidia.com/v1",
    api_key=Secret.from_token("<your-api-key>"),
    model_arguments={
        "temperature": 0.2,
        "top_p": 0.7,
        "max_tokens": 1024,
    },
))
pipe.connect("retriever", "prompt_builder.documents")
pipe.connect("prompt_builder", "llm")

res=pipe.run({
    "prompt_builder": {
        "query": query
    },
    "retriever": {
        "query": query
    }
})

print(res)
```

## Additional References

🧑‍🍳 Cookbook: [Haystack RAG Pipeline with Self-Deployed AI models using NVIDIA NIMs](https://haystack.deepset.ai/cookbook/rag-with-nims)
